data-science

This repository contains hands-on machine learning models implemented using Python libraries like scikit-learn, TensorFlow, and more. Each notebook is designed to demonstrate fundamental concepts in machine learning and can serve as a learning resource for beginners and enthusiasts.

Structure

The project structure is organized as follows:

1. Notebooks: Jupyter notebooks containing step-by-step implementations of various machine learning algorithms.
2. Code: Python scripts showcasing different ML models, their implementation, and usage.
3. Datasets: Relevant datasets used across different labs for training and evaluation.
4. Resources: Additional resources, such as documentation, guides, or reference materials.

Content

Each module covers a specific machine learning topic:

1. Classification: Implementations of classification algorithms (e.g., Decision Trees, Random Forest) using real-world datasets.
2. Regression: Implementations of regression models (e.g., Linear Regression, Polynomial Regression) using real-world datasets.
3. Clustering: Implementations of clustering algorithms (e.g., K-means, DBSCAN, and hierarchical clustering).
4. Neural Networks: Introduction to building neural networks using TensorFlow/Keras for image classification task.